Mage Data

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  • Taming the Agentic AI Beast

    Taming the Agentic AI Beast

    Taming the Agentic AI Beast: How CISOs Can Transform Security Nightmares into Strategic Victories

    Agentic AI is reshaping enterprise ecosystems. As these systems connect with more services and vendors, security risks intensify. Forward-thinking CISOs, however, can turn this challenge into a strategic advantage by leveraging Mage Data’s robust security foundation.

    The Perfect Storm: Why Agentic AI Keeps CISOs Awake at Night

    The cybersecurity world is buzzing with both excitement and anxiety about agentic AI. Unlike traditional AI models, agentic AI operates with autonomy—making decisions, accessing multiple systems, and acting with minimal human oversight. This new reality introduces an entirely different risk calculus for CISOs.

    Agentic AI can access, and process sensitive information governed by strict regulatory and contractual controls. If left unchecked, this autonomy can lead to data exposure, regulatory violations, or even operational disruptions.

    Key emerging concerns include:

    • Data Exposure at Scale: AI agents often ingest far more data than necessary, increasing the potential for overreach and unintended disclosure.
    • Shadow AI Proliferation: Unapproved AI deployments by business units or users bypass traditional security and governance processes.
    • Compliance Blind Spots: Autonomous AI activity makes it harder to track and control data flows, heightening GDPR, CCPA, and HIPAA compliance risks.
    • Multi-Agent Chaos: In multi-agent environments, uncoordinated actions can create cascading vulnerabilities that traditional controls aren’t equipped to handle.

    These factors create the “perfect storm” that keeps security leaders awake at night—unless the right data security foundation is in place.

    The Foundation-First Approach: Why Data Security Must Come Before AI Innovation

    Securing agentic AI isn’t just about controlling the AI itself. It’s about securing the data landscape these agents will inevitably access. CISOs must strengthen data protection strategies before granting autonomous systems the keys to enterprise data.

    Mage Data’s core philosophy is clear: agentic AI security is fundamentally a data security problem. You can’t secure what you can’t see, and you can’t protect what you haven’t classified or controlled.

    The Mage Data Shield: Six Critical Capabilities for Agentic AI Security

    1. Intelligent Data Discovery and Classification

    Mage Data’s Data Discovery™ solution goes beyond traditional regex-based tools by using AI and NLP for context-aware sensitive data discovery. With over 70 prebuilt classifications covering PII, PHI, and financial data, it builds a precise foundation for governance.

    Key Value for Agentic AI: CISOs gain full visibility into what data AI agents are accessing, enabling granular, risk-based access controls.

    2. Dynamic Data Masking for Real-Time Protection

    Autonomous AI activity demands adaptive protection. Mage Data’s Dynamic Data Masking applies real-time, role-based protection, ensuring agents see only the minimum required data—nothing more. It supports six deployment modes and over 70 anonymization methods while maintaining referential integrity.

    Key Value for Agentic AI: AI agents get functional access without exposing sensitive fields, significantly reducing the blast radius of incidents.

    3. Static Data Masking for Development and Testing

    Training or testing AI systems with real production data creates unnecessary exposure. Mage Data’s Static Data Masking delivers realistic but anonymized datasets across structured and unstructured formats, maintaining utility without compromising privacy.

    Key Value for Agentic AI: Enables safe development and testing of AI models without exposing actual customer or regulated data.

    4. Focused Database Activity Monitoring

    Agentic AI can execute complex, multi-step data access patterns that evade traditional defenses. Mage Data’s Database Monitoring is designed to focus on sensitive data access, integrating directly with discovery tools to prioritize critical assets.

    Key Value for Agentic AI: Detect abnormal AI agent behaviors—such as mass retrievals or unauthorized access—before they become incidents.

    5. Proactive Data Minimization

    Reducing the amount of sensitive data accessible to AI agents limits potential damage. Mage Data’s Data Minimization automatically identifies, tokenizes, and archives aged or inactive data.

    Key Value for Agentic AI: Minimizes exposure by ensuring only relevant and current data is available in production environments.

    6. Comprehensive Test Data Management

    Testing agentic AI requires robust data without regulatory risk. Mage Data’s Test Data Management (TDM) solution creates anonymized, de-identified, and referentially intact datasets that mimic real production environments.

    Key Value for Agentic AI: Supports safe, large-scale testing and validation of agentic systems while maintaining compliance.

    The Integration Advantage: Why Platform Thinking Matters

    Mage Data stands apart because these capabilities are natively integrated:

    • Consistent Protection: Unified data classifications and policies across all environments.
    • Reduced Complexity: A single-pane-of-glass interface simplifies governance.
    • Faster Implementation: Predefined templates and automated workflows speed deployment.
    • Better Compliance: Centralized controls ensure adherence to regulatory frameworks.

    A platform-driven strategy allows CISOs to manage agentic AI risk holistically, not through fragmented point solutions.

    The Strategic Imperative: From Reactive to Proactive

    Agentic AI adoption isn’t on the horizon already accelerating. CISOs can no longer afford to react after incidents occur. The organizations that thrive will be those that:

    • Enable Innovation: Give development teams secure, policy-governed access to the data they need.
    • Ensure Compliance: Maintain regulatory adherence as AI systems scale.
    • Reduce Risk: Contain potential impact by controlling sensitive data exposure.
    • Build Trust: Demonstrate security leadership to customers, regulators, and partners.

    In Conclusion

    Only 42% of executives surveyed are balancing AI development with appropriate security investments. Just 37% have formal processes in place to assess AI security before deployment. The agentic AI revolution is here, and it’s moving fast. The question isn’t if your organization will adopt agentic AI, but whether you’ll be ready with the right security foundations. Mage Data’s integrated platform provides the robust data security layer CISOs need to turn agentic AI from a security nightmare into a strategic advantage. By building intelligent discoveries, masking, monitoring, and minimization, enterprises can innovate safely—without losing sleep. The future belongs to organizations that harness agentic AI while protecting their most asset: data.

    Get to know about Mage Data’s solutions

    Ready to build the data security foundation for your agentic AI initiatives?

  • Zero Trust for AI: The Enterprise Implementation Guide for CISOs

    Zero Trust for AI: The Enterprise Implementation Guide for CISOs

    Artificial intelligence is transforming every enterprise function — from predictive analytics to automated decision-making — but it’s also creating a new frontier of risk. A recent study shows that 38% of employees share sensitive data with AI tools without authorization, and organizations are now deploying an average of 50 new AI applications daily.

    In this hyper-connected environment, trust can no longer be assumed.

    Enter Zero Trust for AI — a strategic security framework that extends the “never trust, always verify” principle to autonomous systems. Organizations that have successfully adopted this approach are realizing up to 92% ROI within six months, cutting data breach risks by 50%, and strengthening resilience across the enterprise.

    Traditional security models were built for controlled environments. AI, by contrast, introduces dynamic agents, self-learning models, and decision engines that operate beyond predictable perimeters.

    According to NIST SP 800-207, Zero Trust requires every identity — human or machine — to be continuously verified before gaining access. When extended to AI, this principle demands authentication, behavioral validation, and trust scoring for algorithms, models, and data pipelines alike.

    The CISA Zero Trust Maturity Model (v2.0) outlines five core pillars essential to secure AI operations:

    1. Identity Management for AI Agents – Enforcing authentication for AI service accounts and models.
    2. Device and Infrastructure Security – Protecting GPUs, TPUs, and model-training clusters.
    3. Network Micro-Segmentation – Isolating training and inference environments with least-privilege controls.
    4. Secure AI Development – Incorporating code integrity and container security into model pipelines.
    5. End-to-End Data Protection – Safeguarding sensitive data across the AI lifecycle.

    This architectural shift — from guarding boundaries to validating behaviors — defines the new enterprise standard for AI-driven trust governance

    Despite well-documented risks, 89% of enterprises have no visibility into AI usage across their environments. This lack of oversight has led to an explosion of shadow AI — more than 74,500 unapproved AI tools discovered across global firms, growing 5% month over month.

    Compounding the issue is a cybersecurity workforce gap of 4.8 million professionals, with AI-specific security roles taking 21% longer to fill than traditional IT positions. Nearly 58% of organizations face budget constraints, while 77% struggle with overlapping compliance mandates from GDPR, CCPA, and emerging AI-specific regulations.

    Meanwhile, 11% of corporate data input into ChatGPT and other LLMs contains confidential or regulated content — personally identifiable information (PII), protected health information (PHI), and proprietary source code — often leaving the enterprise perimeter entirely.

    These realities demand a new operational model: Zero Trust built for AI scale, speed, and autonomy.

    Successful enterprise adoption typically unfolds over four phases, balancing innovation with control:

    1. Assess & Plan – Conduct Zero Trust maturity assessments (CISA ZTMM v2.0), map AI data flows, and identify critical assets.
    2. Foundation & Visibility – Deploy monitoring and certificate-based identity management for AI agents; classify data across environments.
    3. Policy & Automation – Implement automated policy enforcement, continuous compliance monitoring, and AI-aware threat detection.
    4. Optimization & Integration – Integrate AI security telemetry into enterprise SIEM platforms (e.g., Microsoft Sentinel, Splunk) to enable predictive analytics and autonomous incident response.

    This phased approach enables CISOs to scale security incrementally — aligning protection with business priorities and regulatory timelines.

    As AI systems evolve toward autonomy, privacy and security must shift from access restriction to trust governance — ensuring that AI behaves ethically, transparently, and in alignment with enterprise intent.

    Enterprises must extend the traditional CIA triad (Confidentiality, Integrity, Availability) to include:

    • Authenticity – Verifying AI identity and provenance.
    • Veracity – Ensuring accurate, explainable, and auditable AI outputs.
    • Legibility – Making AI decisions interpretable for human oversight.

    Mage Data enables this new paradigm by embedding explainability, lineage, and ethical boundaries within the data fabric itself — empowering organizations to build AI systems that are both powerful and principled.

    CISOs should approach Zero Trust for AI through a phased, outcome-driven strategy.

    • Immediate (0–3 months): Conduct AI usage audits to uncover shadow deployments, establish incident response plans for threats like model poisoning or prompt injection, and implement basic monitoring for unauthorized AI activity. Translate AI risks into business metrics to engage the board in financial and reputational impact.
    • Medium-term (3–12 months): Build AI governance frameworks aligned with Zero Trust principles, deploy AI-specific security and DLP tools, and develop automated policy enforcement and incident playbooks for model compromise. Establish risk quantification methods linking AI exposure to business outcomes.
    • Long-term (12+ months): Create AI Security Centers of Excellence, implement enterprise wide Zero Trust architectures, maintain continuous risk assessments, and cultivate an AI security culture through training and awareness.

    This phased approach ensures enterprises can innovate confidently while maintaining control, compliance, and trust across the AI ecosystem.

    Zero Trust for AI marks a critical evolution in enterprise security architecture — driven by the rapid expansion of AI adoption and the sophisticated risks these systems introduce. With shadow AI usage increasing by 5% each month and 27.4% of AI-input data containing sensitive information, organizations can no longer afford reactive approaches. The proven 50% reduction in breach of risks among enterprises that have implemented Zero Trust frameworks underscores the urgency and value of proactive adoption.

    Success in this domain depends on balancing innovation with protection — through phased implementation strategies that prioritize high-value AI use cases, define measurable security outcomes, and sustain long-term cultural and technological transformation.

    Enterprises that proactively integrate Zero Trust principles into their AI ecosystems will not only strengthen their defenses but also unlock a strategic competitive advantage. The convergence of AI capabilities with Zero Trust design transforms security from a compliance requirement into a business enabler, empowering organizations to lead confidently in the age of intelligent enterprise transformation.

    Contact us to schedule a personalized demo of Mage Data’s Zero Trust–driven Data Protection Platform.

    Download our Data Security Brief to learn how leading enterprises are embedding trust, compliance, and AI security into their digital transformation journey.

  • Revolutionizing Core Banking with AI Agent–Driven Test Data Generation

    Revolutionizing Core Banking with AI Agent–Driven Test Data Generation

    It’s 9:15 AM on a critical Wednesday. Your team needs to validate a new interest calculation module before a Friday compliance deadline. This requires complex mortgage test scenarios with specific rate adjustment histories – and of course, using real customer data isn’t an option.

    In the past, this situation might have triggered days of scrambling, manual data anonymization, and compromised testing. But at XYZ bank, it now unfolds very differently:

    QA Lead to DevOps Engineering AI Agent: “I need a test environment with at least 50 jumbo ARM loans that have recently experienced a rate adjustment. We need to validate the new interest calculation module against these scenarios”

    Within minutes, an orchestrated team of AI agents begin collaborating:

    Test Data Orchestrator Agent: “I’ll coordinate creation of a targeted test dataset. What anonymization level Is required for this testing?”

    DevOps Engineer Agent: “Standard PII anonymization with preservation of loan terms and payment history. Focus on interest adjustment patterns rather than customer profiles.”

    Test Data Orchestrator Agent to TDM Agent: “Create a focused test dataset for interest calculation validation. We need 50+ jumbo ARM loans with recent rate adjustments. Standard PII anonymization required with preservation of all financial attributes affecting interest calculation.”

    When an edge case is discovered, the agents adapt:

    TDM Agent: “I’ve identified the needed loan scenarios, but some have unusual structuring with multiple linked accounts. Should I include these complex structures or focus only on standard ARM loans?”

    Test Data Orchestrator Agent: “Include the complex structures as they’re important edge cases for testing. Ensure account linkages are preserved in the anonymized dataset.”

    Less than two hours later – a process that once took days – the QA team receives a fully compliant, perfectly structured test environment:

    DevOps Engineer Agent to QA Lead: “Your test environment has been created with 64 jumbo ARM loans matching your specifications. All necessary financial attributes are preserved while customer data is fully anonymized. Environment URL and access credentials have been shared over email.”

    This isn’t science fiction. This is how our TDM Agent technology is transforming test data management for financial institutions – and it is revolutionizing their ability to develop and deliver banking services at a faster pace than their competitors.

    Core banking modernization initiatives face a persistent struggle: development teams need production-quality data to ensure thorough testing, but using actual customer data creates significant compliance and security risks. Traditional approaches to this challenge fall short:

    • Manual data anonymization is labor-intensive, error-prone, and often results in data that no longer reflects real-world scenarios
    • Synthetic data generation frequently misses edge cases and complex relationships crucial for banking applications
    • Static test data becomes stale and fails to represent changing production patterns

    Mage Data’s TDM Agent was developed to address these critical banking industry challenges. Our clients no longer need to wait for weeks for test environment or compromise on data quality to maintain compliance.

    Mage Data has created a collaborative ecosystem of specialized AI Agents that work together to create perfect test environments. At the center of this ecosystem is our TDM Agent, which provides advanced privacy and data transformation capabilities that integrate seamlessly with existing banking systems.

    Mage Data’s agent ecosystem is architected to balance specialization with seamless collaboration. The TDM Agent sits at the center of the environment creation process, with other agents aiding it:

    1. DevOps Engineer Agent interfaces with human engineers and translates business requirements into technical specifications
    2. Test Data Orchestrator Agent coordinates the overall workflow and manages communication between specialized agents
    3. TDM Agent provides the critical privacy and data transformation capabilities at the core of the solution
      1. Analyze the production database schema to identify sensitive data points
      2. Subsetting the data to include representative examples of all loan types and statuses
      3. Applying sophisticated anonymization across related tables while preserving business rules
      4. Generating synthetic transactions where needed to fill gaps in history
    4. Data Modeling Agent verifies data integrity, relationships and business rule preservation
    5. Compliance Auditor Agent ensures all processes adhere to strict regulatory requirements
    6. Test Automation Agent validates the final environment against functional requirements

    This agent ecosystem replaces traditionally siloed processed with fluid, coordinated action focused on delivering perfect testing environments.

    For Testing Owners
    • Comprehensive scenario coverage with all edge cases represented
    • Consistent test data across development, QA and UAT environments
    • On-demand environment refreshes in hours rather than days or weeks
    • Self-service capabilities for testing teams who need specialized data scenarios
    For Data Privacy Officers
    • Zero exposure of PII in any test environment
    • Detailed audit trail of all anonymization techniques applied
    • Consistent policy enforcement across all applications and environments
    For AI Implementation Teams

    For banks building their AI capabilities, Mage Data’s ecosystem represents an architectural pattern that they can deploy across other functions:

    • Decentralized Intelligence with specialized agents for specific tasks
    • Extensible architecture where new capabilities can be added as agents
    • Standardized collaboration using the Agent2Agent protocol
    • Human-in-the-loop options for exception handling and approvals

    Banking technology leaders stand at a crossroads – continue with traditional, labor-intensive test data approaches that slow innovation, or embrace an AI-powered, privacy-first TDM solution that accelerates development while enhancing compliance.

    1. Assess your current test data challenge – Quantify the time spent creating test environments and any privacy near-misses or incidents
    2. Identify a high-value pilot application – Look for areas where test data quality directly impacts customer experience or compliance
    3. Engage cross-functional stakeholders – Bring together testing, privacy, development, and compliance leaders
    4. Run a pilot of the TDM Agent – See Mage Data’s agent ecosystem in action in a banking specific scenario

    In today’s banking landscape, the competitive edge belongs to institutions that can innovate rapidly while maintaining impeccable data privacy standards. Mage Data’s TDM Agent technology isn’t just an IT solution – it is a strategic business capability that delivers measurable advantages in speed, quality, and compliance

  • Securing SAP: Why Data Protection Matters Now

    Securing SAP: Why Data Protection Matters Now

    Introduction

    SAP systems serve as the operational core for global enterprises, processing an astounding $87 trillion in financial transactions annually across more than 230,000 customers worldwide. This foundational role in the global economy makes these systems exceptionally attractive targets for sophisticated cyber adversaries. Yet despite their critical importance, many organizations continue to operate under the dangerous misconception that commercial ERP solutions like SAP are inherently secure “by default.”

    The stark reality tells a different story. The average cost of an ERP security breach has surged to over $5.2 million, representing a significant 23% increase from previous years. More alarming still, ransomware incidents specifically targeting compromised SAP systems have increased by 400% since 2021. With 52% of organizations confirming a breach in the past year and 70% experiencing at least one significant cyber attack in 2024, the question is no longer if your SAP environment will be targeted, but when—and whether you’ll be prepared.

    The Escalating SAP Security Challenge: Problems Demanding Strategic Solutions

    • Challenge 1: Comprehensive Sensitive Data Discovery Across Complex SAP Landscapes
      The Problem: Organizations struggle to identify where sensitive data resides within their vast SAP ecosystems. Research reveals that 31% of organizations lack the necessary tools to identify their riskiest data sources, with an additional 12% uncertain about their capabilities. This visibility gap becomes critical when considering that SAP environments often contain hundreds of database tables with thousands of columns housing personally identifiable information (PII), financial data, and other regulated information.

      Mage Data’s Solution: Mage Data’s Sensitive Data Discovery module provides intelligent, AI-powered Discovery specifically designed for SAP environments. The platform supports over 80 out-of-the-box data classifications covering names, social security numbers, addresses, emails, phone numbers, financial records, and health data. For SAP-specific deployments, Sensitive Data Discovery automatically discovers sensitive data across SAP ECC, S/4HANA, and RISE environments, supporting popular SAP databases including HANA, Oracle, and SQL Server. The solution goes beyond basic pattern matching, utilizing Natural Language Processing (NLP) and deterministic scoring mechanisms to minimize false positives – achieving a 95% reduction in investigative columns between discovery iterations.

    • Challenge 2: Production Data Exposure in Non-Production Environments
      The Problem: Development, testing, and analytics teams require realistic data to ensure application functionality, yet using production data in these environments creates substantial compliance and security risks. Traditional approaches often result in either unusable synthetic data or dangerous exposure of sensitive information across multiple environments.

      Mage Data’s Solution: With Mage’s comprehensive Static Data Masking capabilities addresses this challenge with over 60 anonymization algorithms including Masking, Encryption, and Tokenization. For SAP environments specifically, the platform maintains referential integrity across SAP modules and relational structures while offering context-preserving masking and Format-Preserving Encryption (FPE). The solution supports in-place, in-transit, as-it-happens, and REST API-based anonymization approaches, allowing organizations to choose the optimal method for their SAP architecture. Customer success stories demonstrate the platform’s enterprise scalability—one implementation protected 2.6 terabytes of data across 264 tables with 6,425 columns and over 1.6 billion rows in just 29 hours.

    • Challenge 3: Real-Time Production Data Protection Without Performance Impact
      The Problem: Protecting sensitive data in production SAP environments requires sophisticated access controls that don’t disrupt business operations. Traditional proxy-based approaches introduce security vulnerabilities and performance bottlenecks, while static solutions fail to provide the granular, role-based access control needed for complex SAP user hierarchies.

      Mage Data’s Solution: Mage’s Dynamic Data Masking module offers six different deployment approaches for production SAP environments: embedded in database, database via proxy, application via database masking, application via API, application via REST API, and application via web proxy. This flexibility ensures seamless integration regardless of SAP architecture. The platform provides real-time, role-based masking directly at both the SAP database layer and application/UI layer across SAP GUI, SAP Fiori, and SAP UI5-based applications. With over 70 anonymization methods available, organizations can implement the optimal balance between security, performance, and data usability while maintaining consistent protection across their entire SAP landscape.

    • Challenge 4: Third-Party Risk and Supply Chain Vulnerabilities
      The Problem: A staggering 63% of all data breaches in 2024 involved vendors, making third-party risk management a critical concern for SAP environments. The interconnected nature of modern SAP deployments, with extensive integrations to external applications and service providers, creates multiple potential entry points for attackers.

      Mage Data’s Solution:       Mage’s centrally managed, platform-agnostic approach ensures consistent data masking protection across all data repositories and environments, whether on-premises or cloud-hosted. The distributed agent architecture enables protection to be applied anywhere in the data flow while maintaining centralized policy management. This capability is particularly crucial for SAP RISE environments and hybrid cloud deployments where data flows across multiple vendor boundaries. The unified platform approach reduces the complexity that comes from managing multiple disparate security tools—addressing the challenge faced by 54% of organizations that currently use four or more tools for data risk management.
    • Challenge 5: Regulatory Compliance and Audit Readiness

      The Problem: Global data privacy regulations continue to intensify, with GDPR fines alone surpassing €4.5 billion since 2018. CPRA penalties for intentional violations will increase to $7,500 per violation in 2025, while the annual revenue threshold for compliance has been lowered to $25 million. Organizations struggle with fragmented compliance approaches and lack integrated visibility into their data protection posture.

      Mage Data’s Solution: Mage provides pre-configured Data Masking templates specifically designed to comply with GDPR, CPRA, HIPAA, PCI-DSS, and other industry-specific regulations. The platform’s unified architecture provides a single pane of glass for managing discovery, classification, masking policies, access control, and monitoring across SAP and non-SAP systems. The integrated approach extends from sensitive data discovery through data lifecycle management, including automated data retirement capabilities through Data Retirement for inactive sensitive data. This comprehensive coverage ensures organizations can demonstrate compliance readiness and respond effectively to regulatory inquiries or audits.

    What Makes Mage Data’s SAP Protection Unique

    Research demonstrates that organizations implementing specialized third-party SAP security tools experience 42% fewer successful attacks compared to those relying solely on native capabilities. Mage Data’s differentiation lies in its comprehensive, integrated approach that addresses the complete data protection lifecycle within SAP environments.
    Unlike point solutions that address individual aspects of data security, Mage provides a unified platform that seamlessly integrates discovery, masking, monitoring, and compliance across both production and non-production SAP environments. The platform’s distributed agent architecture ensures that sensitive data never leaves the target environment during protection processes, while centralized policy management maintains consistency across complex hybrid SAP deployments.
    Mage’s deep SAP expertise is evident in its support for the full spectrum of SAP environments—from legacy ECC systems to modern S/4HANA and cloud-based RISE deployments. The platform’s ability to provide both database-level and application-level protection ensures comprehensive coverage regardless of how users access SAP data, whether through traditional SAP GUI, modern Fiori interfaces, or custom applications.
    The platform’s scalability has been proven in enterprise environments processing terabytes of data across thousands of tables and millions of records, with performance optimizations that minimize impact on critical business operations. This combination of comprehensive functionality, proven scalability, and SAP-specific expertise positions Mage Data as the strategic partner for organizations serious about protecting their SAP investments.

    Conclusion

    In conclusion, Mage Data delivers a comprehensive, multi-layered data security framework that protects sensitive information throughout its entire lifecycle. The first step begins with data classification and discovery, enabling organizations to locate and identify sensitive data across environments. This is followed by data cataloging and lineage tracking, offering a clear, traceable view of how sensitive data flows across systems.
    In non-production environments, Mage Data applies static data masking (SDM) to generate realistic yet de-identified datasets, ensuring safe and effective use for testing and development. In production, a Zero Trust model is enforced through dynamic data masking (DDM), database firewalls, and continuous monitoring—providing real-time access control and proactive threat detection.
    This layered security approach not only supports regulatory compliance with standards such as GDPR, HIPAA, and PCI-DSS but also minimizes risk while preserving data usability. By integrating these capabilities into a unified platform, Mage Data empowers organizations to safeguard their data with confidence—ensuring privacy, compliance, and long-term operational resilience.

    Contact us to schedule a personalized demo of Mage’s SAP Data Protection platform and discover how we can help secure your organization’s most critical data assets.

    • Mage Data’s Solution: Mage’s centrally managed, platform-agnostic approach ensures consistent data masking protection across all data repositories and environments, whether on-premises or cloud-hosted. The distributed agent architecture enables protection to be applied anywhere in the data flow while maintaining centralized policy management. This capability is particularly crucial for SAP RISE environments and hybrid cloud deployments where data flows across multiple vendor boundaries. The unified platform approach reduces the complexity that comes from managing multiple disparate security tools—addressing the challenge faced by 54% of organizations that currently use four or more tools for data risk management.

  • Navigating HSM Compliance in the Bharat BFSI Sector with Mage Data

    Navigating HSM Compliance in the Bharat BFSI Sector with Mage Data

    Introduction

    The Bharat BFSI sector is navigating a complex regulatory landscape, with stringent requirements for data protection and privacy. Hardware Security Modules (HSMs) are crucial for securing cryptographic keys and ensuring compliance, but they can be challenging to implement and integrate with existing systems. Mage Data offers a powerful solution that complements HSMs, simplifying compliance, enhancing security, and optimizing performance.

    Understanding HSMs and Their Role

    HSMs are specialized hardware devices that safeguard cryptographic keys and perform cryptographic operations within a secure, tamper-proof environment. They are essential for:

    • Key Generation & Storage: Generating strong keys and storing them securely.
    • Key Management: Managing the lifecycle of keys (generation, storage, distribution, and destruction).
    • Cryptographic Operations: Performing encryption, decryption, digital signatures, and authentication.
    • Access Control: Implementing strict access controls to prevent unauthorized use.

    Organizations often face a trio of challenges when implementing HSMs

    • Regulatory Complexity: Navigating the maze of data protection regulations can be daunting. GDPR, CCPA, HIPAA, PCI DSS, and various industry-specific mandates create a complex web of requirements. Data localization laws add another layer of complexity, often requiring keys to be stored within specific geographical boundaries. Keeping up with these evolving regulations and ensuring consistent compliance can be a major headache.
    • Integration Complexity: Many legacy systems weren’t designed with HSMs in mind. Integrating these older systems with modern HSMs can require complex API integrations, middleware solutions, and custom development. Compatibility issues with older cryptographic libraries further complicate the process, leading to increased costs and implementation timelines.




    • Performance Overhead: Cryptographic operations, while essential, can introduce latency. In high-volume environments, this can lead to performance bottlenecks, impacting application responsiveness and user experience. Real-time transaction signing, SSL/TLS encryption, and blockchain key management are just a few examples of workloads that can be affected by HSM latency.

    Mage Data: Enhancing HSMs with Advanced Capabilities

    Mage Data complements HSMs by adding a layer of advanced data security and management capabilities:

    Secure Tokenization and De-tokenization:

    • Mage Data leverages HSMs to protect the mapping between tokens and real data, ensuring that even if tokens are compromised, the original data remains secure.
    • It provides granular control over token usage and facilitates secure de-tokenization when authorized access to the real data is required.

    Controlled Access to Sensitive Data:

    • Mage Data’s masking capabilities, combined with HSM access controls, enable fine-grained control over who can access sensitive data and in what form (masked or original).
    • This allows for secure data sharing and collaboration while protecting sensitive information.

    Performance Optimization:

    • Mage Data can offload certain masking operations to the HSM, leveraging its cryptographic capabilities for enhanced performance, especially for high-volume data masking tasks.

    Centralized Platform:

    • Mage Data integrates with HSMs to provide a centralized platform for managing both masking policies and cryptographic keys, simplifying data security management across the organization.


    How Mage Data Complements HSMs (with Diagram):Benefits of the Integrated Solution:

    • Enhanced Security: Multi-layered protection through HSMs and Mage Data’s masking and tokenization.

    • Simplified Compliance: Meeting regulatory requirements for data protection and key management.

    • Optimized Performance: Efficient masking and cryptographic operations.

    • Centralized Management: Streamlined administration of data security policies and keys.

    • Reduced Risk: Minimizing the risk of data breaches and unauthorized access.

    Conclusion:

    Mage Data complements HSMs by providing advanced data security capabilities, simplifying compliance, and optimizing performance. This integrated approach enables organizations in the Bharat BFSI sector to protect their sensitive data, meet regulatory requirements, and unlock the full potential of their data assets.

  • Are There Good Open Source Tools for Sensitive Data Discovery?

    Are There Good Open Source Tools for Sensitive Data Discovery?

    Open-source tools have come into their own in the past decade, including tools for sensitive data discovery. What used to be the domain of large corporations has been democratized, and teams of passionate people can (and do) develop amazing tools. However, with the ever-growing number of data privacy and security laws, the stakes around data classification have never been higher. Getting sensitive data discovery right has significant consequences…so it’s critical you understand what you’re getting with these tools, and how you can use them in ways that will keep you (and your customer and employee data) safe.

    What Makes Data Discovery Tools Open-Source?

    We’ve already covered what makes software open source in this article in depth , but we want to give a quick recap of what we’ll be discussing here. Unlike closed-source tools, free sensitive data discovery tools are released under a license allowing others to use and alter the software for their purposes freely. Generally, instead of being created and owned by a corporation, open-source software is developed by a passionate community, who collaborate to create new features and often determine future direction democratically.

    Many talented people are working on great open-source sensitive data discovery tools like OpenDataDiscovery, ReDiscovery, DataDefender, and more. Consequently, to answer the question in the title of the article, there are good open-source tools for sensitive data discovery. However, that’s not necessarily the question you should be asking—instead, you should be trying to determine if they’ll be right for your company. And one of the best ways to make that determination is through a SWOT Analysis, taking a detailed look at the Strengths, Weaknesses, Opportunities, and Threats that come from using open-source tools for data discovery.

    Data Discovery Tools: Strengths

    First up are the strengths—the things that open-source data discovery tools do well.

    Interoperability and Flexibility

    Because there are generally a variety of perspectives involved in open-source tools, there’s often little incentive to hide features and programs behind walled gardens. In this case, that often translates into tools with a wide range of integrations and connections for data. And even when a certain database type isn’t supported, these tools often provide a way for you to build the integration yourself, ensuring that getting data is rarely a roadblock.

    Price

    And, of course, the best price you can get for anything is free. That could mean you save a bit of money or free up resources to invest in areas that need it more. Whatever the case, it will be hard to get a better deal than what you get with open-source tools.

    Data Discovery Tools: Weaknesses

    Of course, no software is perfect. Here are some things open-source data discovery tools don’t always do well.

    Unknown Development Cycles

    Many B2B tools feature a regular and predictable development cycle. Some open-source projects are very organized, and others are less so. Regardless, there’s no guarantee that a feature or fix will come out on time—or even that there will be a roadmap to start with. The inherent unpredictability of the process can sometimes be frustrating.

    Enterprise Readiness

    As companies grow, their data environments become more complex at an exponential rate. Not all open-source data discovery tools can handle the complexity of a modern enterprise data environment. And of those that can, not all will be able to provide the detailed reporting and compliance options that companies need to meet their legal obligations.

    Data Discovery Tools: Opportunities

    With open-source tools, companies have some opportunities they wouldn’t necessarily have with paid tools.

    Opportunity to Influence Development

    As a user of an open-source tool, you’re part of the community developing it. While you still won’t have ultimate control over its development direction, you’ll likely have the ability to vote on next steps and generally have greater influence on the development process than you would over most paid tools. This can provide the opportunity to get the features you need faster than traditional development.

    Customization via Forking

    And if the community doesn’t prioritize your needs, you’re allowed to fork, or make a copy of, the underlying source code, allowing your company to continue development in the way it sees fit. That’s an option you’re typically never going to have with traditional software.

    Data Discovery Tools: Threats

    Of course, there are some downsides to open-source tools.

    Poor/Nonexistent Customer Support

    Because open-source tools are generally community-run projects where people work for free, customer support is not guaranteed. People, including other users, are often very helpful through online forums, but that often doesn’t rise to the same level of support you would get from just about any paid tool. And when you have a serious issue with your software, this problem can keep you from resolving it quickly. And as a reminder, 99 percent success in data discovery isn’t good enough, and could open you up to serious legal ramifications. If you’re having an issue with sensitive data discovery, failing to find a quick solution can be an expensive mistake.

    Rogue Developers

    While it’s unlikely that the developers of an open-source data discovery tool would insert malware or create serious security vulnerabilities, it’s not unheard of. But even if no one acts maliciously, there’s a real chance that the project will eventually be abandoned without warning. And abandoned software won’t receive security updates or new features and could leave you looking for a new solution once more.

    How Mage Data Helps with Sensitive Data Discovery

    If you’ve reached the end of the above SWOT analysis feeling that the strengths and opportunities far outweigh the weaknesses and risks, then there’s a good chance that there’s a great open-source sensitive data discovery tool out there for you. But that won’t be the case for all businesses. It doesn’t mean that the tools are bad, just that they are not a good fit for all business contexts.

    Remember that sensitive data discovery is the starting point of good data management. There are so many more things that need to be done to keep data safe and companies compliant. Here at Mage, we’ve developed a world-class AI-powered sensitive data discovery tool, that’s part of a larger suite of tools designed to manage data from discovery all the way to retirement. If that sounds more like what you need, sign up for a free consultation today to learn more about what Mage Data can do for you.

  • Data Security Platform: Securing the Digital Perimeter

    Data Security Platform: Securing the Digital Perimeter

    In today’s data-driven world, organizations face increasing challenges in protecting sensitive information while ensuring compliance with stringent data privacy regulations. The exponential growth of data has also led to a higher risk of unauthorized access, breaches and cyber-attacks being faced by organizations. In such a scenario, protecting sensitive information is a top priority for businesses, and the use of Data Security Platforms (DSP) has emerged as a crucial component in the battle against data threats. This article delves into the significance of a DSP, its role in compliance with data privacy regulations, and the common challenges faced during adoption.

    What is a Data Security Platform?

    A Data Security Platform is designed to protect sensitive and valuable data from unauthorized access, breaches and other security threats. Gartner defines Data Security Platforms (DSPs) as products and services characterized by data security offerings that target the integration of the unique protection requirements of data across data types, storage silos and ecosystems.

    Gartner, in their report “2023 Strategic Roadmap for Data Security Platform Adoption” lists 6 capabilities required for a Data Security Platform (Fig 1)

    Fig.1

    Let us go through each of these capabilities in detail:

    Data Discovery and Classification

    Data Discovery and Classification involves the automated scanning and analysis of an organization’s data repositories to identify and categorize sensitive data. This process helps organizations understand where sensitive information resides, such as personal identifiable information (PII), financial data, intellectual property, or other confidential data.

    The data classification process tags data with relevant labels indicating its sensitivity level and compliance requirements. For example, data might be classified as “Confidential,” “Internal Use Only,” or “Public.” This classification enables organizations to enforce appropriate access controls, data protection measures, and data handling policies based on the data’s sensitivity. It also aids in compliance with data protection regulations since organizations can ensure that sensitive data is treated according to the applicable laws.

    Data Access Controls

    Data Access Controls are mechanisms that ensure only authorized users have appropriate access to specific data. This component plays a vital role in preventing unauthorized access to sensitive information, reducing the risk of data breaches and insider threats.

    Role-based access control (RBAC) is a common approach in data security platforms, where permissions are assigned based on the user’s role within the organization. Access rights can be granted or revoked based on job functions, ensuring that users only have access to data they need to perform their tasks.

    Data Access Controls work hand-in-hand with the data classification process, as the access privileges are often determined based on the sensitivity level of the data. Strong access controls help ensure that data is only accessible to authorized individuals and minimize the risk of data leaks or unauthorized disclosures.

    Data Masking

    Data Masking is the process of concealing original sensitive data by replacing it with realistic but fictional data. The purpose of data masking is to create a structurally similar version of the data without revealing the actual information. This is particularly important for non-production environments like testing or development, where real data is not needed.

    Data Masking is commonly used to protect sensitive data while ensuring that applications and processes can still function realistically with representative data. This prevents the exposure of actual sensitive data during testing or other non-production activities, reducing the risk of data breaches resulting from mishandling or accidental leaks in lower-security environments.

    Database Encryption

    Database Encryption involves converting plaintext data into ciphertext using encryption algorithms, rendering the data unreadable and useless without the appropriate decryption key.

    At-rest encryption ensures that data stored on disk or in a database is protected even if physical storage media is compromised. In contrast, in transit encryption safeguards data as it is transmitted over networks, preventing eavesdropping or interception by unauthorized parties.

    Database encryption adds an extra layer of security, making it significantly harder for attackers to access sensitive data, even if they gain unauthorized access to the underlying infrastructure.

    Database Activity Monitoring

    Database Activity Monitoring (DAM) is a real-time surveillance mechanism that captures and records user activities and behaviors related to database access and usage. It tracks queries, data modifications, login attempts, and other interactions with the database.

    DAM helps detect suspicious or unauthorized activities, such as unauthorized attempts to access sensitive data or unusual data access patterns. When abnormal behavior is detected, the system can trigger alerts to security teams, enabling them to respond promptly to potential security threats and prevent data breaches.

    Data Risk Analytics

    Data Risk Analytics involves the use of advanced analytics and machine learning techniques to assess security risks associated with an organization’s data environment. By analyzing patterns, trends, and historical data, this component can identify potential vulnerabilities and predict security risks before they escalate.

    Data Risk Analytics helps security teams gain insights into potential data security issues, such as weak access controls, suspicious user behaviors, or unsecured data repositories. These insights enable organizations to take proactive measures to strengthen their overall data security posture and mitigate potential risks before they lead to security incidents or data breaches.

    The Advantages of a Data Security Platform (DSP)

    In an era where data breaches and privacy concerns dominate headlines, organizations need to fortify their data security measures across the entire enterprise data landscape to safeguard their reputation, build customer trust, and sustain financial stability. A Data Security Platform (DSP) provides a centralized approach to data security, enabling businesses to efficiently manage data protection across various systems and applications. It serves as a comprehensive solution that comprises various components enabling data security across the sensitive data lifecycle. . By adopting a DSP, organizations can

    Figure 2

    Ensuring Compliance with Data Privacy Regulations

    The implementation of a DSP significantly aids organizations in complying with various data privacy regulations:

    GDPR Compliance

    The GDPR mandates stringent data protection measures, including data minimization, purpose limitation, and user consent management. A DSP helps organizations meet these requirements by implementing encryption, access controls, and consent management mechanisms.

    CCPA and Other Privacy Regulations

    The California Consumer Privacy Act (CCPA) and similar regulations empower individuals with greater control over their personal information. A DSP enables organizations to manage user preferences, handle data subject requests, and maintain auditable records for compliance.

    Emerging Regulations

    As new privacy regulations continue to emerge globally, a DSP provides a future-proof solution by offering flexibility and scalability to adapt to evolving compliance requirements. This ensures organizations can stay ahead of the regulatory curve.

    Overcoming Challenges during DSP Adoption

    While adopting a DSP offers significant advantages, organizations may face certain challenges:

    Integration Complexity

    Integrating a DSP with existing IT infrastructure and applications can be complex. To overcome this challenge, organizations should carefully plan the integration process, seek vendor support, and collaborate closely with IT teams to ensure a seamless deployment.

    Employee Training and Awareness

    The successful adoption of a DSP depends on the knowledge and awareness of employees. Organizations should invest in comprehensive training programs to educate employees about the DSP’s functionalities, data protection best practices, and the importance of compliance.

    Balancing Security and Usability

    Organizations may face the challenge of balancing data security measures with usability and productivity. It is crucial to strike the right balance by implementing security controls that protect data without hindering operational efficiency.

    Keeping Pace with Changing Regulations

    Data privacy regulations continue to evolve, necessitating ongoing monitoring and updates to the DSP. Organizations should stay informed about regulatory changes, actively engage with legal and compliance experts, and collaborate with the DSP vendor to ensure the platform remains up to date.

    Conclusion

    In an era where data security and compliance with privacy regulations are critical, a Data Security Platform (DSP) emerges as a comprehensive solution for organizations. By adopting a DSP, organizations can fortify their data security measures, ensure compliance with regulations, and mitigate the risks associated with data breaches. Although challenges may arise during adoption, proactive planning, employee training, and ongoing monitoring can help organizations overcome them and achieve data security excellence in today’s complex digital landscape.

    At Mage Data, we focus our efforts on empowering organizations with the tools and technologies to secure their data throughout its lifecycle – from creation and storage to processing and transmission. With Mage Data, you get access to a Data Security Platform that has been ranked as the Gartner Peer Insights Customer’s Choice for 3 years in a row and has also been named as an Overall Leader for Data Security Platforms by KuppingerCole. If you’re on the lookout for a comprehensive Data Security Platform that meets your organization’s IT strategic goals, feel free to reach out.

  • Why Data Breaches Are So Costly…And So Difficult to Prevent

    Why Data Breaches Are So Costly…And So Difficult to Prevent

    No one in a large organization wants to hear the news that there has been a data breach, and that the organization’s data has been compromised. But many are reluctant to spend a significant portion of their budget on appropriate preventative measures. Why?

    The reason usually comes down to two misconceptions. Either the leadership of the organizations assumes that a data breach is unlikely, or that, if a breach were to happen, their risk exposure would be minimal and the problem easily fixed.

    The truth is that, today, data breaches are inevitable…and much more costly. Companies are often much more exposed than they know, which means that the potential costs of data compromise are much higher than assumed—and so is ROI of preventive measures.

    Data Breaches Are Inevitable

    In 2022, there were over 1,800 data compromises of U.S. companies alone, impacting some 422 million individuals. This is four times the number of compromises reported just a decade ago.

    Think about this risk as you would a similar risk, such as a fire at a building or a plant. As the saying goes, companies don’t carry insurance because they think something bad might happen—they get insurance because bad things do happen. On a long enough timeline, it’s a virtual guarantee that something bad will strike your business. Yes, fires are rare, but they happen, and they are devastating. The same goes for data breaches.

    But here is one important way in which a fire is different from a data breach. The risk of a fire scales linearly with the number of locations you have; the risk that unsecure data poses to your business scales exponentially, even if you have a small number of total records. As a result, many companies’ data management practices may create millions or hundreds of millions of dollars of risk. Most are not even aware of it.

    Systems Are Complex, and There is More Risk Than You Imagine

    Gone are the days when a company has a server or two in a server closet, housing their data. Today’s companies have multiple connected systems, many of which are spinning up cloud environments and transferring data on a daily basis.

    In these scenarios, data duplication creates a huge risk for companies should their systems be compromised. For example, a single company might have both client records and employee records, all of which are duplicated in a live “production” environment, a testing environment, and a data lake for analytics purposes. A single breach could potentially expose all of this data, multiplying the risk.

    (For a more full accounting of the math here, see our whitepaper on the ROI of Risk Reduction, now available for download.)

    What is the Actual Cost of Exposed Data?

    So data compromise is inevitable, and companies have richer stores of data these days. They real question is this: Does the cost associated with a data breach exceed the budget needed to prevent one?

    One of the very best resources for understanding what drives the cost behind a data breach is IBM’s annual Cost of a Data Breach report. The worldwide average cost of a breach in 2021 was $4.24 million, the highest average total cost in the history of the report. That works out to about $180 per record for customer information, and $176 per record for employee data.

    Importantly, it wasn’t just direct remediation costs that contributed to this total. Thirty-eight percent of the total cost was attributable to “customer turnover, lost revenue due to system downtime, and the increasing cost of acquiring new business due to diminished reputation,” which suggests that the pain caused by a breach lasts for years beyond the initial incident.

    Again, having duplicate records drives up costs here. A single customer, for example, might be tied to data that “lives” in several systems, both production environments and non-production environments. Which means that a single customer is not just $180 worth of risk, but potentially 4-5 times that amount.

    Prevention Needs to be Modern, Too

    In short, data breaches are much larger and more complex than they were even a decade or two ago. That also makes them much more costly. It also means that the methods for preventing breaches and reducing risk need to be similarly modern and complex.

    For example, data discovery needs to be a part of any security efforts. Discovering all databases and all instances of records in a working organization can be a massive challenge; AI-based tools are now necessary to both find and identify all the data in play.

    Once data is discovered, there are various tools that can be used to protect that data, including encryption, masking, and access controls. Which tools are appropriate for which data sets depend on factors such as how often the data needs to be accessed, who will need to access it, and system performance requirements.

    That said, there is a set procedure that should be followed to reduce the risk of exposure. Here at Mage Data, we’ve honed that procedure over the years; in some cases, we can reduce the dollar-amount risk by more than 90%.

    To see what this procedure is, and to see the math behind this reduction of risk, download our white paper, The ROI of Risk Reduction for Data Breaches.

  • Ensuring Consumer Data Privacy in Financial Services

    Ensuring Consumer Data Privacy in Financial Services

    It used to be—even just a few years ago—that consumer advocacy groups were complaining that legislation had not “caught up” with technology, and that huge data privacy gaps existed. But today, gaps in data privacy regulation are only half of the problem, as multiple legislative bodies have worked tirelessly to close the gaps. As financial services becomes an increasingly digital industry, finserv organizations now have to prepare for tomorrow’s regulatory landscape as well.

    In particular, finserv organizations are finding that consumers of financial services themselves are sensitive to the collection and processing of their information. Fortunately, financial services ties healthcare as the most-trusted sector for data privacy, according to research from McKinsey & Company. Less fortunately, the percentage of respondents who trust data privacy practices in the financial services industry is still only 44%.

    Financial institutions can keep pace with these simultaneous rises in digital banking activity and data privacy legislation by taking proactive measures:

    • Commit to learning what’s protected under all relevant consumer data privacy laws.
    • Consider how advances in digital banking and online payments will affect privacy concerns going forward.
    • Give customers transparency regarding data collection, protection, sharing, and use.
    • Implement data security and data privacy throughout the entire data lifecycle.
    • Appoint chief privacy officers and give them the resources required to develop thorough privacy practices.

      The nature of the financial services industry makes the stakes particularly high, but a commitment to best practices instills confidence and competence

    What to Know About Data Privacy in Financial Services

    Individuals divulge a great deal of personal information during online transactions. This is necessary because financial services organizations must have identity proofing to tie every transaction to a valid entity. Failure to collect, confirm, and store the appropriate data increases the likelihood of fraud. The need to collect, handle, and store data creates tension, as doing so means that financial institutions absorb responsibility for data privacy and data security.

    The Current State of Financial Privacy Regulation

    The uncertainty around data privacy for financial services is highlighted by the sheer volume of the applicable legislation. Instead of one universal law, financial institutions are accountable to numerous regulations, especially when they operate internationally. The following are several of the most common data privacy regulations impacting the financial services industry:

    • Gramm-Leach-Bliley Act (GLBA)
    • Payment Card Industry Data Security Standard (PCI DSS)
    • Sarbanes-Oxley Act (SOX)
    • California Consumer Protection Act (CCPA)
    • Payment Services Directive (PSD2)
    • General Data Protection Regulation (GDPR)
    • Financial Privacy Rule from the Federal Trade Commission (FTC)
    • Regulations from the New York State Department of Financial Services (NYDFS)
    • Consumer Data Right (Australia)
    • Monetary Authority of Singapore (MAS)

    The list above is a lot to absorb, but it isn’t even exhaustive. Organizations like the NYDFS and FTC work continuously to regulate financial services and products as threats and best practices evolve.

    When Financial Privacy Regulations Collide

    At times, consumer privacy laws seem to contradict each other. For example, the CCPA gives individuals the right to delete (or request deletion of) some of their information, but there are exceptions. One such exception occurs when financial institutions need the information in question to operate. Reconciling consumer privacy regulations and finance-specific privacy regulations—especially when one regulation supersedes another—requires constant diligence and a comprehensive data protection strategy.

    Evolving Data Privacy Responsibilities in the Finance Industry

    Data protection strategies must be works in progress if they’re not to become outdated. The current state of data privacy for financial services organizations doesn’t stay current for very long. Legislative bodies are creating and modifying regulations, which is far from the only concern. Data breaches, technological adoption, and data privacy best practices are evolving every bit as quickly.

    4 Steps Toward Data Privacy for Financial Services Organizations

    Pressing data security challenges in the financial services industry include third-party risks, data transfers, and compliance issues. A comprehensive strategy allows financial institutions to prepare, implement, and maintain an integrated data privacy platform:

    PrepareImplementMaintain
    1. Sensitive Data Discovery and Classification
    2. Appoint a chief privacy officer or work with a third-party organization to keep up with changing regulations    		1. Static Data Masking
    2. Dynamic Data Masking
    3. Additional Privacy Enhancing Technologies    		1. Database Activity Monitoring
    2. Data Subject Access Rights Requests
    3. Database Visualization
    4. Database Firewall
    1. Sensitive Data Discovery

    By definition, a privacy plan can only be comprehensive when it covers all information throughout the enterprise or institution. Data discovery is a vital first step. Diligent data discovery brings hidden or forgotten information into the light.

    Accurate data discovery and thoughtful data classification make privacy plans more intentional. The process answers some of the most pressing data protection questions:

    • Which data is necessary, and which is best disposed of?
    • Which individuals, applications, and third parties need access to data?
    • How sensitive is the information retained?
    • What do applications and analysts need to get from the data to operate as intended?

      Answers to these questions provide the visibility required to protect data as efficiently as possible.
    2. Data Protection

    It’s often necessary to retain sensitive information. The financial institution absorbs responsibility for data privacy and security in these cases. Techniques like encryption, tokenization, and masking anonymize data in various states and environments. Dynamic data masking, for example, protects data in use without slowing down the analytics team. Static data masking is a common choice for data at rest, or whenever it’s best to permanently replace sensitive data without the potential for re-identification (also known as de-anonymization).

    To close the loop, Privacy enhancing technologies (PETs) facilitate analysis of the privacy plan itself: with data sensitivity scorecards, incremental data scanning, and audit-ready reporting. Interconnected and interdependent networks of PETs allow financial services institutions to meet their various data privacy goals and expectations.

    After selecting PETs or other data protection techniques to meet all privacy requirements, it’s time to bring everything into a manageable hub.

    3. Integrating Data Security and Data Privacy

    Beyond well-integrated, a data security platform must be scalable enough to protect all data sources, production, and non-production environments. Bringing data privacy into a central hub makes it more difficult for anything to fall through the cracks. Such a platform ties preparation, implementation, and maintenance together.

    Increased visibility, manageable breach notifications, and adequate compliance reporting take the guesswork out of data privacy. Scalable solutions and excellent knowledge of the current state of an organization’s data privacy efforts make it easier to evolve. Adapting to changing regulations doesn’t have to mean starting over.

    Finally, integrated data security and data privacy platforms keep the burden of database activity monitoring to a minimum. Data subject access rights requests, alerts, and notifications appear in one central location. From there, it’s easier to add database visualization tools to help spend less time identifying priorities and more time working on them.

    4. Data Privacy and Consumer Consent

    Collecting and using personal data is generally prohibited unless the subject consents or the data processing is expressly allowed by regulation. Even when a financial organization has every right to collect information, it may be required to provide privacy notices. Under the GLBA, for example, organizations are required to provide notice of how they collect and use information. An organization must provide notice to the data subject even when the data processing does not require the subject’s consent.

    Data Privacy Technology for the Finance Industry

    Regulatory technology, or RegTech, is working behind the scenes to help financial services firms regain customer trust. Financial institutions that navigate the customer trust landscape gain competitive advantages by protecting their reputations.

    Societal factors like social distancing forced banks to accelerate digital transformation plans, and RegTech offers a much-needed boost. Consumer insights and regulatory compliance are twin differentiators. Legislative bodies and individual users are most satisfied when financial institutions go above and beyond minimum requirements and invest proactively in data protection solutions.

    Getting Started With Data Privacy for Financial Services

    The rising legislative and market-driven demand for data privacy separates financial institutions with stringent data protection plans. A demonstrable commitment to data privacy helps organizations win trust to increase their user base, and avoid fines to protect profits. Comprehensive programs address privacy, security, and data risks as interconnected and interdependent issues. To see how data security and data privacy for financial services combine, contact Mage Data for a demo.

  • What Are the Consequences of Non-Compliance with Data Privacy Laws

    What Are the Consequences of Non-Compliance with Data Privacy Laws

    It seems like a new data privacy law is going into effect every day, so keeping track of the requirements these laws impose on businesses can be daunting. However, the sheer volume of new laws doesn’t excuse companies from complying with all that apply to them. The consequences can be severe when companies are non-compliant with data privacy laws.

    What Penalties Can Companies Face for Non-Compliance with Data Privacy Laws?

    Non-compliance with data privacy laws can be costly. Let’s look at some of the largest penalties ever levied to understand what companies may face when they fail in their compliance efforts.

    The Largest GDPR Fine

    On July 22, 2021, the National Commission for Data Protection in Luxembourg announced a €746,000,000 fine against Amazon. After receiving 10,000 complaints about the company’s practices, the Luxembourgian agency launched an investigation that revealed Amazon was using customer data for targeted advertising in ways that weren’t covered by its privacy policy. While Amazon has rightfully pointed out that there hadn’t been a breach of customer data, this fine highlights that laws about data have moved beyond security and into protecting customer privacy. Companies that don’t transition their policies to cover these new requirements have a good chance of ending up like Amazon here, with massive fines despite no external breach.

    The CCPA Means Business

    Since the CCPA  gives companies 30 days to cure their operations after being notified of a violation, fines are less likely to occur. So, when they do happen, it’s a strong sign of serious malfeasance by the company. On August 24, 2022, the Attorney General of California, responsible for enforcing the CCPA, announced that Sephora was being fined $1.2 million after failing to cure its issues during the 30-day window. According to the Attorney General, Sephora allowed third-party vendors to track customer activity on its website and app and failed to disclose that the activity was being tracked, that Sephora was being paid for the tracking, and that Sephora failed to provide an opt-out option, as required by law. This case emphasizes that consent in data processing is more important than ever, and providing legally required notification and opt-out procedures is vital.

    Why do Companies Fail in Compliance?

    Because the financial penalties can be so severe, companies must understand the common personal-data mistakes that businesses make that can result in regulatory action.. and how to avoid those mistakes.

    They Don’t Understand or Keep up With the Laws

    Compliance with data laws is like paying your taxes. Just as not understanding the complexities of tax law isn’t an excuse to not pay taxes owed, not having a complete understanding of data privacy. laws doesn’t excuse you from its provisions. Unlike tax law, which only has major changes every few years or so, data privacy is a rapidly evolving organism. Every year, more and more data privacy laws are passed. These range from international requirements, like the GDPR in the EU, to national laws, like those in China and Singapore, to state and provincial laws in countries with a federal system.

    The scary reality for most companies is that if the information you’re using to manage your data privacy policies is even six months out of date, your company could be at serious risk of regulatory action based on a brand-new law. For companies, it is critical to ensure that the people you have in charge of your data privacy policies are keeping up with the latest developments to ensure your company is always kept safe.

    They Don’t Manage Risk Well

    The reality is that if you make a minor mistake in handling personal data just once, it’s unlikely that you’ll be caught. Even if you are, regulatory agencies would have to decide if it’s worth their time to bring enforcement action, given that there’s much bigger fish to fry. That doesn’t mean they won’t penalize your company, but the odds are low. Now, take that same minor mistake and scale it up so that instead of just doing it once, you’ve done it on a hundred thousand or a million records. Now, your “little” mistake has grown so much that regulators can’t ignore it.

    That’s not to say that small companies can never get in legal trouble for this issue. But what companies sometimes realize is that the risk grows exponentially with size rather than linearly. For one, businesses tend to process exponentially more records as they grow and use them in more ways. But oversight also gets much more difficult. Larger companies have more departments and more teams, which can make oversight far more difficult. Any one of those can create a data processing nightmare, so companies that fail to empower their data privacy policymakers to enforce the rules and audit teams for compliance may take on far more risk than they realize.

    They Don’t Understand the Consequences

    Some companies would barely notice a fine if caught violating data privacy laws. But there’s more to regulatory action than just the fine that is levied. Reputational damage is real. When your customers or clients hear that you’re mishandling their data, you can suffer real losses. Your brand image, which can take years or even decades of performance to build, can be cut down in a moment. Companies that don’t take this threat seriously can trick themselves into believing they can easily survive any regulatory action over data privacy. Don’t fall into that trap!

    How Mage Data Helps Companies with Data Privacy

    From the examples above, it’s clear that companies need a solid plan for keeping up with changing data privacy laws and ensuring that they remain compliant. However, the plan is only half of the equation. Your data privacy teams need the tools to help them execute their vision. That’s where Mage Data comes in. Mage Data provides tools to help companies with data privacy and security from the database to the front end. They start working out of the box but can also be highly customized to meet an enterprise’s needs. Schedule a demo with Mage Data today to learn more.